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Record W3203678481 · doi:10.1002/cjp2.241

Characterizing the tumor microenvironment in rare renal cancer histological types

2021· article· en· W3203678481 on OpenAlex
Naoise C. Synnott, Maria Luana Poeta, Manuela Costantini, Ruth M. Pfeiffer, Mengying Li, Yelena Golubeva, Scott M. Lawrence, Karun Mutreja, Carla Azzurra Amoreo, Małgorzata Dąbrowska, Giuseppe Simone, Edoardo Pescarmona, Petra H. Lenz, Mary E. Olanich, Máire A. Duggan, Mustapha Abubakar, Vito Michele Fazio, Michele Gallucci, Steno Sentinelli, Maria Teresa Landi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Pathology Clinical Research · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of Calgary
FundersNational Cancer Institute
KeywordsCD68CD20PathologyImmunohistochemistryTumor microenvironmentMedicineStromal cellImmune systemCancerBiologyInternal medicineImmunology

Abstract

fetched live from OpenAlex

The tumor microenvironment (TME), including immune cells, cancer-associated fibroblasts, endothelial cells, adjacent normal cells, and others, plays a crucial role in influencing tumor behavior and progression. Here, we characterized the TME in 83 primary renal tumors and matched metastatic or recurrence tissue samples (n = 15) from papillary renal cell carcinoma (pRCC) types 1 (n = 20) and 2 (n = 49), collecting duct carcinomas (CDC; n = 14), and high-grade urothelial carcinomas (HGUC; n = 5). We investigated 10 different markers of immune infiltration, vasculature, cell proliferation, and epithelial-to-mesenchymal transition by using machine learning image analysis in conjunction with immunohistochemistry. Marker expression was compared by Mann-Whitney and Kruskal-Wallis tests and correlations across markers using Spearman's rank correlation coefficient. Multivariable Poisson regression analysis was used to compare marker expression between histological types, while accounting for variation in tissue size. Several immune markers showed different rates of expression across histological types of renal carcinoma. Using pRCC1 as reference, the incidence rate ratio (IRR) of CD3+ T cells (IRR [95% confidence interval, CI] = 2.48 [1.53-4.01]) and CD20+ B cells (IRR [95% CI] = 4.38 [1.22-5.58]) was statistically significantly higher in CDC. In contrast, CD68+ macrophages predominated in pRCC1 (IRR [95% CI] = 2.35 [1.42-3.9]). Spatial analysis revealed CD3+ T-cell and CD20+ B-cell expressions in CDC to be higher at the proximal (p < 0.0001) and distal (p < 0.0001) tumor periphery than within the central tumor core. In contrast, expression of CD68+ macrophages in pRCC2 was higher in the tumor center compared to the proximal (p = 0.0451) tumor periphery and pRCC1 showed a distance-dependent reduction, from the central tumor, in CD68+ macrophages with the lowest expression of CD68 marker at the distal tumor periphery (p = 0.004). This study provides novel insights into the TME of rare kidney cancer types, which are often understudied. Our findings of differences in marker expression and localization by histological subtype could have implications for tumor progression and response to immunotherapies or other targeted therapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.477
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it